2020-05-20 - Mutation Rate Sweeps - Experiment Analysis
Analysis Dependencies
library(ggplot2) # (Wickham, 2016)
library(tidyr) # (Wickham and Henry, 2020)
library(dplyr) # (Wickham et al., 2020)
library(reshape2) # (Wickham, 2007)
library(cowplot) # (Wilke, 2019)We conducted these analyses using the following computing environment:
print(version)## _
## platform x86_64-apple-darwin15.6.0
## arch x86_64
## os darwin15.6.0
## system x86_64, darwin15.6.0
## status
## major 3
## minor 6.2
## year 2019
## month 12
## day 12
## svn rev 77560
## language R
## version.string R version 3.6.2 (2019-12-12)
## nickname Dark and Stormy NightSetup
data_path <- "./data/agg_data.csv"
agg_data <- read.csv(data_path, na.strings="NONE")
agg_data$BIT_FLIP_PROB <- as.factor(agg_data$BIT_FLIP_PROB)
agg_data$DRIFT <- agg_data$TOURNAMENT_SIZE==1
# Compute expected changes per generation
exp_change <- function(mag, interval) {
if (interval == 0) { return(0) }
else { return(mag / interval) }
}
# Label
chg_rate_label <- function(mag, interval, drift) {
if (drift) { return("drift") }
else if (interval == 0) { return("0") }
else { return(paste(mag, interval, sep="/")) }
}
agg_data$EXP_CHANGE_PER_GEN <- as.factor(mapply(exp_change,
agg_data$CHANGE_MAGNITUDE,
agg_data$CHANGE_FREQUENCY
))
agg_data$chg_rate_label <- factor(mapply(chg_rate_label,
agg_data$CHANGE_MAGNITUDE,
agg_data$CHANGE_FREQUENCY,
agg_data$DRIFT),
levels=c("drift", "0", "1/256", "1/128",
"1/64", "1/32", "1/16", "1/8",
"1/4", "1/2", "1/1", "2/1",
"4/1", "8/1", "16/1", "32/1",
"64/1", "128/1", "256/1",
"512/1", "1024/1", "2048/1",
"4096/1"))
theme_set(theme_cowplot())
data_nk_phase0 <- filter(agg_data, GRADIENT_MODEL==0 & evo_phase == 0 & update==50000)
data_nk_phase1 <- filter(agg_data, GRADIENT_MODEL==0 & evo_phase == 1 & update==60000)
data_gradient_phase0 <- filter(agg_data, GRADIENT_MODEL==1 & evo_phase == 0 & update==50000)
data_gradient_phase1 <- filter(agg_data, GRADIENT_MODEL==1 & evo_phase == 1 & update==60000)Gradient model - Experiment phase 1
ggplot(data_gradient_phase0,
aes(x=BIT_FLIP_PROB,
y=coding_sites,
color=BIT_FLIP_PROB)) +
geom_boxplot() +
xlab("Bit Flip Rate") +
scale_y_continuous(name="# coding sites in best organism",
limits=c(0, 130),
breaks=seq(0, 130, 10)) +
facet_wrap(~ chg_rate_label) +
ggtitle("Gradient Fitness Model") +
theme(legend.position="none",
axis.text.x=element_text(angle=90))
ggplot(data_gradient_phase0,
aes(x=chg_rate_label,
y=coding_sites,
color=chg_rate_label)) +
geom_boxplot() +
xlab("Change rate") +
scale_y_continuous(name="# coding sites in best organism",
limits=c(0, 130),
breaks=seq(0, 130, 10)) +
facet_wrap(~ BIT_FLIP_PROB) +
ggtitle("Gradient Fitness Model") +
theme(legend.position="none",
axis.text.x=element_text(angle=90))
#######
ggplot(data_gradient_phase0,
aes(x=BIT_FLIP_PROB,
y=neutral_sites,
color=BIT_FLIP_PROB)) +
geom_boxplot() +
xlab("Bit Flip Rate") +
scale_y_continuous(name="# neutral sites in best organism") +
facet_wrap(~ chg_rate_label) +
ggtitle("Gradient Fitness Model") +
theme(legend.position="none",
axis.text.x=element_text(angle=90))+
ggsave("neutral-gradient.pdf")## Saving 7 x 5 in image
ggplot(data_gradient_phase0,
aes(x=chg_rate_label,
y=neutral_sites,
color=chg_rate_label)) +
geom_boxplot() +
xlab("Change rate") +
scale_y_continuous(name="# neutral sites in best organism") +
facet_wrap(~ BIT_FLIP_PROB) +
ggtitle("Gradient Fitness Model") +
theme(legend.position="none",
axis.text.x=element_text(angle=90)) +
ggsave("neutral-gradient-2.pdf")## Saving 7 x 5 in image
######
ggplot(data_gradient_phase0,
aes(x=BIT_FLIP_PROB,
y=genome_length,
color=BIT_FLIP_PROB)) +
geom_boxplot() +
xlab("Bit Flip Rate") +
scale_y_continuous(name="genome length",
limits=c(0, 1024)) +
facet_wrap(~ chg_rate_label) +
ggtitle("Gradient Fitness Model") +
theme(legend.position="none",
axis.text.x=element_text(angle=90))
ggplot(data_gradient_phase0,
aes(x=BIT_FLIP_PROB,
y=fitness,
color=BIT_FLIP_PROB)) +
geom_boxplot() +
xlab("Bit Flip Rate") +
scale_y_continuous(name="fitness") +
facet_wrap(~ chg_rate_label) +
ggtitle("Gradient Fitness Model") +
theme(legend.position="none",
axis.text.x=element_text(angle=90))
Gradient model - experiment phase 2
ggplot(data_gradient_phase1,
aes(x=BIT_FLIP_PROB,
y=fitness,
color=BIT_FLIP_PROB)) +
geom_boxplot() +
xlab("Bit Flip Rate") +
scale_y_continuous(name="fitness") +
facet_wrap(~ chg_rate_label) +
ggtitle("Gradient Fitness Model - Phase 2") +
theme(legend.position="none",
axis.text.x=element_text(angle=90))
ggplot(data_gradient_phase1,
aes(x=coding_sites,
y=fitness,
color=BIT_FLIP_PROB)) +
geom_point() +
xlab("Coding sites") +
scale_y_continuous(name="fitness") +
facet_wrap(~ chg_rate_label) +
ggtitle("Gradient Fitness Model - Phase 2") +
theme(legend.position="top")
NK fitness model - experiment phase 1
ggplot(data_nk_phase0,
aes(x=BIT_FLIP_PROB,
y=coding_sites,
color=BIT_FLIP_PROB)) +
geom_boxplot() +
xlab("Bit Flip Rate") +
scale_y_continuous(name="# coding sites in best organism",
limits=c(0, 130),
breaks=seq(0, 130, 10)) +
facet_wrap(~ chg_rate_label) +
ggtitle("NK Fitness Model") +
theme(legend.position="none",
axis.text.x=element_text(angle=90))
ggplot(data_nk_phase0,
aes(x=chg_rate_label,
y=coding_sites,
color=chg_rate_label)) +
geom_boxplot() +
xlab("Change rate") +
scale_y_continuous(name="# coding sites in best organism",
limits=c(0, 130),
breaks=seq(0, 130, 10)) +
facet_wrap(~ BIT_FLIP_PROB) +
ggtitle("NK Fitness Model") +
theme(legend.position="none",
axis.text.x=element_text(angle=90))
ggplot(data_nk_phase0,
aes(x=BIT_FLIP_PROB,
y=genome_length,
color=BIT_FLIP_PROB)) +
geom_boxplot() +
xlab("Bit Flip Rate") +
scale_y_continuous(name="genome length",
limits=c(0, 1024)) +
facet_wrap(~ chg_rate_label) +
ggtitle("NK Fitness Model") +
theme(legend.position="none",
axis.text.x=element_text(angle=90))
ggplot(data_nk_phase0,
aes(x=BIT_FLIP_PROB,
y=fitness,
color=BIT_FLIP_PROB)) +
geom_boxplot() +
xlab("Bit Flip Rate") +
scale_y_continuous(name="fitness") +
facet_wrap(~ chg_rate_label) +
ggtitle("NK Fitness Model") +
theme(legend.position="none",
axis.text.x=element_text(angle=90))
NK model - experiment phase 2
ggplot(data_nk_phase1,
aes(x=BIT_FLIP_PROB,
y=fitness,
color=BIT_FLIP_PROB)) +
geom_boxplot() +
xlab("Bit Flip Rate") +
scale_y_continuous(name="fitness") +
facet_wrap(~ chg_rate_label) +
ggtitle("NK Fitness Model - Phase 2") +
theme(legend.position="none")
ggplot(data_nk_phase1,
aes(x=coding_sites,
y=fitness,
color=BIT_FLIP_PROB)) +
geom_point() +
xlab("Coding sites") +
scale_y_continuous(name="fitness") +
facet_wrap(~ chg_rate_label) +
ggtitle("NK Fitness Model - Phase 2") +
theme(legend.position="top")
Gradient model - gene overlap by environment similarity
env_sim_data_path <- "./data/env_sim_gene_overlap_static.csv"
env_sim_data <- read.csv(env_sim_data_path, na.strings="NONE")
env_sim_data$BIT_FLIP_PROB <- as.factor(env_sim_data$BIT_FLIP_PROB)
env_sim_data$DRIFT <- env_sim_data$TOURNAMENT_SIZE==1
env_sim_data$EXP_CHANGE_PER_GEN <- as.factor(mapply(exp_change,
env_sim_data$CHANGE_MAGNITUDE,
env_sim_data$CHANGE_FREQUENCY
))
env_sim_data$chg_rate_label <- factor(mapply(chg_rate_label,
env_sim_data$CHANGE_MAGNITUDE,
env_sim_data$CHANGE_FREQUENCY,
env_sim_data$DRIFT),
levels=c("drift", "0", "1/256", "1/128",
"1/64", "1/32", "1/16", "1/8",
"1/4", "1/2", "1/1", "2/1",
"4/1", "8/1", "16/1", "32/1",
"64/1", "128/1", "256/1",
"512/1", "1024/1", "2048/1",
"4096/1"))
theme_set(theme_cowplot())
env_sim_data$gene_pair_target_similarity_factor <- as.factor(env_sim_data$gene_pair_target_similarity)
env_sim_data$gene_pair_overlap_factor <- as.factor(env_sim_data$gene_pair_overlap)
env_sim_data_selection <- filter(env_sim_data, TOURNAMENT_SIZE==8)
env_sim_data_drift <- filter(env_sim_data, TOURNAMENT_SIZE==1)Taking a closer look at genetic architecture distributions
# Occurences of environment similarity
library(patchwork)##
## Attaching package: 'patchwork'## The following object is masked from 'package:cowplot':
##
## align_plotsa <-
ggplot(env_sim_data_selection, aes(x=gene_pair_target_similarity_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene target similarity") +
facet_wrap(~BIT_FLIP_PROB) +
ggtitle("Selection")## Warning: Ignoring unknown parameters: binwidth, bins, padb <-
ggplot(env_sim_data_drift, aes(x=gene_pair_target_similarity_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene target similarity") +
facet_wrap(~BIT_FLIP_PROB) +
ggtitle("Drift")## Warning: Ignoring unknown parameters: binwidth, bins, pad(a | b) + ggsave("pairwise-gene-target.pdf", width=15, height=10)
a <-
ggplot(env_sim_data_selection, aes(x=gene_pair_overlap_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene overlap") +
facet_wrap(~BIT_FLIP_PROB) +
ggtitle("Selection")## Warning: Ignoring unknown parameters: binwidth, bins, padb <-
ggplot(env_sim_data_drift, aes(x=gene_pair_overlap_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene overlap") +
facet_wrap(~BIT_FLIP_PROB) +
ggtitle("Drift")## Warning: Ignoring unknown parameters: binwidth, bins, pad(a | b) + ggsave("pairwise-gene-overlap-with0.pdf", width=15, height=10)
a <-
ggplot(filter(env_sim_data_selection, gene_pair_overlap>0), aes(x=gene_pair_overlap_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene overlap") +
facet_wrap(~BIT_FLIP_PROB) +
ggtitle("Selection")## Warning: Ignoring unknown parameters: binwidth, bins, padb <-
ggplot(filter(env_sim_data_drift, gene_pair_overlap>0), aes(x=gene_pair_overlap_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene overlap") +
facet_wrap(~BIT_FLIP_PROB) +
ggtitle("Drift")## Warning: Ignoring unknown parameters: binwidth, bins, pad(a | b) + ggsave("pairwise-gene-overlap-no0.pdf", width=15, height=10)
p <- function(bf_rate, inc_zero) {
d_sel <- env_sim_data_selection
d_drift <- env_sim_data_drift
if (!inc_zero) {
d_sel <- filter(d_sel, gene_pair_overlap>0)
d_drift <- filter(d_drift, gene_pair_overlap>0)
}
a <-
ggplot(filter(d_sel, BIT_FLIP_PROB==bf_rate),
aes(x=gene_pair_overlap_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene overlap") +
facet_wrap(~gene_pair_target_similarity_factor) +
ggtitle(paste("Sel (pairwise gene target sim facets)\nBit flip rate=", bf_rate, sep=""))
b <-
ggplot(filter(d_drift, BIT_FLIP_PROB==bf_rate),
aes(x=gene_pair_overlap_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene overlap") +
facet_wrap(~gene_pair_target_similarity_factor) +
ggtitle(paste("Drift (pairwise gene target sim facets)\nBit flip rate=", bf_rate, sep=""))
return (a | b)
}
p(0.003, TRUE) + ggsave("overlap_x_similarity_0.003.pdf", width=15, height=10)## Warning: Ignoring unknown parameters: binwidth, bins, pad
## Warning: Ignoring unknown parameters: binwidth, bins, pad
p(0.003, FALSE) + ggsave("overlap_x_similarity_0.003-no0.pdf", width=15, height=10)## Warning: Ignoring unknown parameters: binwidth, bins, pad
## Warning: Ignoring unknown parameters: binwidth, bins, pad
p(0.03, TRUE) + ggsave("overlap_x_similarity_0.03.pdf", width=15, height=10)## Warning: Ignoring unknown parameters: binwidth, bins, pad
## Warning: Ignoring unknown parameters: binwidth, bins, pad
p(0.03, FALSE) + ggsave("overlap_x_similarity_0.03-no0.pdf", width=15, height=10)## Warning: Ignoring unknown parameters: binwidth, bins, pad
## Warning: Ignoring unknown parameters: binwidth, bins, pad
p(0.1, TRUE) + ggsave("overlap_x_similarity_0.1.pdf", width=15, height=10)## Warning: Ignoring unknown parameters: binwidth, bins, pad
## Warning: Ignoring unknown parameters: binwidth, bins, pad
p(0.1, FALSE) + ggsave("overlap_x_similarity_0.1-no0.pdf", width=15, height=10)## Warning: Ignoring unknown parameters: binwidth, bins, pad
## Warning: Ignoring unknown parameters: binwidth, bins, pad
All
env_sim_data_path <- "./data/env_sim_gene_overlap.csv"
env_sim_data <- read.csv(env_sim_data_path, na.strings="NONE")
env_sim_data$BIT_FLIP_PROB <- as.factor(env_sim_data$BIT_FLIP_PROB)
env_sim_data$DRIFT <- env_sim_data$TOURNAMENT_SIZE==1
env_sim_data$EXP_CHANGE_PER_GEN <- as.factor(mapply(exp_change,
env_sim_data$CHANGE_MAGNITUDE,
env_sim_data$CHANGE_FREQUENCY
))
env_sim_data$chg_rate_label <- factor(mapply(chg_rate_label,
env_sim_data$CHANGE_MAGNITUDE,
env_sim_data$CHANGE_FREQUENCY,
env_sim_data$DRIFT),
levels=c("drift", "0", "1/256", "1/128",
"1/64", "1/32", "1/16", "1/8",
"1/4", "1/2", "1/1", "2/1",
"4/1", "8/1", "16/1", "32/1",
"64/1", "128/1", "256/1",
"512/1", "1024/1", "2048/1",
"4096/1"))
theme_set(theme_cowplot())
env_sim_data$gene_pair_target_similarity_factor <- as.factor(env_sim_data$gene_pair_target_similarity)
env_sim_data$gene_pair_overlap_factor <- as.factor(env_sim_data$gene_pair_overlap)
env_sim_data <- filter(env_sim_data, GRADIENT_MODEL==1)
env_sim_data_selection <- filter(env_sim_data, TOURNAMENT_SIZE==8)
env_sim_data_drift <- filter(env_sim_data, TOURNAMENT_SIZE==1)# Overlap occurences changing environment
a <-
ggplot(filter(env_sim_data_selection, chg_rate_label=="1/4"), aes(x=gene_pair_overlap_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene overlap") +
facet_wrap(~BIT_FLIP_PROB) +
ggtitle("Selection")## Warning: Ignoring unknown parameters: binwidth, bins, padb <-
ggplot(filter(env_sim_data_drift), aes(x=gene_pair_overlap_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene overlap") +
facet_wrap(~BIT_FLIP_PROB) +
ggtitle("Drift")## Warning: Ignoring unknown parameters: binwidth, bins, pad(a | b) + ggsave("pairwise-gene-overlap-chg-1-4-with0.pdf", width=15, height=10)
a <-
ggplot(filter(env_sim_data_selection, chg_rate_label=="1/4", gene_pair_overlap>0), aes(x=gene_pair_overlap_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene overlap") +
facet_wrap(~BIT_FLIP_PROB) +
ggtitle("Selection")## Warning: Ignoring unknown parameters: binwidth, bins, padb <-
ggplot(filter(env_sim_data_drift, gene_pair_overlap>0), aes(x=gene_pair_overlap_factor)) +
geom_histogram(stat="count") +
ylab("Occurences") +
xlab("Pairwise gene overlap") +
facet_wrap(~BIT_FLIP_PROB) +
ggtitle("Drift")## Warning: Ignoring unknown parameters: binwidth, bins, pad(a | b) + ggsave("pairwise-gene-overlap-chg-1-4-no0.pdf", width=15, height=10)